Enriching Context Information for Entity Linking with Web Data

Journal of Computer Science and Technology - Entity linking (EL) is the task of determining the identity of textual entity mentions given a predefined knowledge base (KB). Plenty of existing...     

高效液相色谱-串联质谱法研究克伦特罗在 猪毛发中残留及蓄积代谢规律

目的 建立一种检测动物毛发中克伦特罗含量的高效液相色谱-串联质谱法(high performance liquid chromatography-tandem mass spectrometry, HPLC-MS/MS)并研究克伦特罗在毛发中的残留及蓄积代谢规律。 方法 选择成长期白猪(平均体质量50 kg), 饲喂克伦特罗日粮, 喂药周期为35 d, 停药32 d。分别于喂药后的d 2、5、7、9、11、13、15、17、19、21、23、25、27、32、35, 每个时间点随机采毛发样品3份, l.0 moL/L氢氧化钠溶液溶解, 叔丁醇:乙酸乙酯(3:7, v:v)震荡萃取, 过阳离子交换(mixed-mode cationic exchange, MCX)固相萃取柱净化后用LC-MS/MS法检测克伦特罗残留量。于休药后的d 0、3、5、7、9、11、13、15、17、19、21、23、25、26、27、30、32, 每个时间点简单随机直抽法处死3只白猪, 取毛发样品处理后用HPLC-MS/MS法检测克伦特罗残留量。结果 克伦特罗在毛发中的残留浓度随用药增加呈上升趋势(喂药d 35为866.75 μg/kg); 克伦特罗在毛发中的残留在休药后浓度先升高后慢慢降低, 休药d 32仍能够检出量为87 μg/kg。 结论 该方法操作简单、重复性好、回收率高, 适于检测动物毛发中的克伦特罗含量, 可为食用动物饲养过程的有效监管提供技术依据。     

基于混合CatfishPSO-LSSVM 特征选择的入侵检测

入侵检测系统面临的主要问题是计算量大,特征选择被引入解决这一问题。针对现有方法的缺点,利用改进的粒子群算法来搜索最优特征子集,提出了一种基于混合CatfishPSO和最小二乘支持向量机的特征选择方法,利用混合的CatfishBPSO和CatfishPSO选择特征子集并同步对LSSVM的参数进行优化,最后建立了一个基于该特征选择方法的入侵检测模型。在KDD Cup 99数据集上进行的实验结果表明该模型的检测性能较高。     

Crowd-Guided Entity Matching with Consolidated Textual Data

Entity matching (EM) identifies records referring to the same entity within or across databases. Existing methods using structured attribute values (such as digital, date or short string values) may fail when the structured information is not enough to reflect the matching relationships between records. Nowadays more and more databases may have some unstructured textual attribute containing extra consolidated textual information (CText) of the record, but seldom work has been done on using the CText for EM. Conventional string similarity metrics such as edit distance or bag-of-words are unsuitable for measuring the similarities between CText since there are hundreds or thousands of words with each piece of CText, while existing topic models either cannot work well since there are no obvious gaps between topics in CText. In this paper, we propose a novel cooccurrence-based topic model to identify various sub-topics from each piece of CText, and then measure the similarity between CText on the multiple sub-topic dimensions. To avoid ignoring some hidden important sub-topics, we let the crowd help us decide weights of different sub-topics in doing EM. Our empirical study on two real-world datasets based on Amzon Mechanical Turk Crowdsourcing Platform shows that our method outperforms the state-of-the-art EM methods and Text Understanding models.     

氨基酸增强型发酵饮料的研制

利用优选的复合菌群对米饭和蚕蛹水解液的混合物进行发酵试验,通过单因素试验和正交试验发现,在米饭发酵12h时添加水解度为20%,添加量为饭重20%的蚕蛹水解液进行混合发酵,最终发酵产品的氨基氮含量可提高11倍,且有效地保留蚕蛹水解液的营养成分,具有发酵饮料的独特风味,可得到一种新型的氨基酸增强型发酵饮料。     

酱卤肉制品加工技术研究进展

酱卤肉制品是一类色泽美观、质地酥软、风味浓郁、口感适中的传统熟肉制品,深受我国广大消费者的喜爱。近年来,人们的饮食观念由过去的片面追求吃饱转变为如今的迫切要求吃好,国家对传统肉制品绿色生产与产业化加工日益重视,对传统酱卤肉制品的安全与营养及绿色加工提出更高要求,很多研究人员正致力于酱卤肉制品加工理论与新技术研究及新产品开发。本文概述了酱卤肉制品的定义、特点和分类,重点阐述了酱卤肉制品加工技术的研究进展,包括酱卤肉制品的注射腌制、滚揉腌制、真空腌制等腌制技术和定量卤制技术,真空冷却技术、超高压、辐照和微波等杀菌技术和防腐保鲜技术,真空、气调、活性和可食性涂膜等包装技术以及有害物质控制检测技术等,指出了我国酱卤肉制品生产中存在的主要问题,并对其发展前景进行预测和展望,以期为酱卤肉制品绿色加工与新产品研发及规模化生产奠定理论基础。     

塑料食品接触材料中非有意添加物检测方法研究进展

食品接触材料(Food contact materials, FCMs) 与食品安全密切相关。FCMs能有效保护食品,防止其腐败变质,但在生产过程中可能会由于一些原因引发食品安全问题,其中从FCMs中迁移出的非有意添加物(non intentionally added substance,NIAS)成为影响食品安全的重要因素而引起社会各界的广泛关注。由于NIAS非常复杂,且相当数量未知,其检测成为我国乃至全球食品接触材料安全评价的关键点及难点。在这篇综述中,以塑料食品接触材料和NIAS为主,介绍塑料材料中的NIAS来源、种类、国内外相关法律法规、近年来研究现状以及分析方法,最后对NIAS检测以及Orbitrap高分辨质谱检测技术的未来发展方向进行了展望。     

An Efficient Framework for Multiple Subgraph Pattern Matching Models

With the popularity of storing large data graph in cloud, the emergence of subgraph pattern matching on a remote cloud has been inspired. Typically, subgraph pattern matching is defined in terms of subgraph isomorphism, which is an NP-complete problem and sometimes too strict to find useful matches in certain applications. And how to protect the privacy of data graphs in subgraph pattern matching without undermining matching results is an important concern. Thus, we propose a novel framework to achieve the privacy-preserving subgraph pattern matching in cloud. In order to protect the structural privacy in data graphs, we firstly develop a k-automorphism model based method. Additionally, we use a cost-model based label generalization method to protect label privacy in both data graphs and pattern graphs. During the generation of the k-automorphic graph, a large number of noise edges or vertices might be introduced to the original data graph. Thus, we use the outsourced graph, which is only a subset of a k-automorphic graph, to answer the subgraph pattern matching. The efficiency of the pattern matching process can be greatly improved in this way. Extensive experiments on real-world datasets demonstrate the high efficiency of our framework.

     

面向视频的细粒度多模态实体链接

随着互联网和大数据的飞速发展,数据规模越来越大,种类也越来越多.视频作为其中重要的一种信息方式,随着近期短视频的发展,占比越来越大.如何对这些大规模视频进行理解分析,成为学界关注的热点.实体链接作为一种背景知识补全方式,可以提供丰富的外部知识.视频上的实体链接可以有效地帮助理解视频内容,从而实现对视频内容的分类、检索、推荐等.但是现有的视频链接数据集和方法的粒度过粗,因此提出面向视频的细粒度实体链接,并立足于直播场景,构建了细粒度视频实体链接数据集.此外,依据细粒度视频链接任务的难点,提出利用大模型抽取视频中的实体及其属性,并利用对比学习得到视频和对应实体的更好表示.实验结果表明,该方法能够有效地处理视频上的细粒度实体链接任务.     

A Generative Model Approach for Geo-Social Group Recommendation

With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem: 1) the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2) users have different influences when in different groups. In this paper, we propose a generative geo-social group recommendation model (GSGR) to recommend points of interest (POIs) for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods.